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Our goal is to synthesize a complete, visually plausible and coherent image. The visible parts of the image serve as a training set to infer the unknown parts. Our method iteratively approximates the unknown regions and composites adaptive image fragments into the image. Values of an inverse matte are used to compute a confidence map and a level set that direct an incremental traversal within the unknown area from high to low confidence. In each step, guided by a fast smooth approximation, an image fragment is selected from the most similar and frequent examples. As the selected fragments are composited, their likelihood increases along with the mean confidence of the image, until reaching a complete image. We demonstrate our method by completion of photographs and paintings.","publishedDate","2003-07-01","publishedYear",2003,"venue","ACM Transactions on Graphics",340,"authors","keywords",[166,167,168],"pdfUrl","topVenue","Image Processing and 3D Reconstruction","Generative Adversarial Networks and Image Synthesis","Advanced Image Processing Techniques","paper_W2998590421","Bilateral mesh denoising","{\"openalex\":\"W2998590421\",\"doi\":\"10.1145/882262.882368\"}","2026-05-16T06:43:26.208233792","2026-05-19T03:19:09.189682013","10.1145/882262.882368","W2998590421","We present an anisotropic mesh denoising algorithm that is effective, simple and fast. This is accomplished by filtering vertices of the mesh in the normal direction using local neighborhoods. Motivated by the impressive results of bilateral filtering for image denoising, we adopt it to denoise 3D meshes; addressing the specific issues required in the transition from two-dimensions to manifolds in three dimensions. We show that the proposed method successfully removes noise from meshes while preserving features. Furthermore, the presented algorithm excels in its simplicity both in concept and implementation.",485,[179,180,181],"Computer Graphics and Visualization Techniques","3D Shape Modeling and Analysis","Image and Signal Denoising Methods","paper_W2016331656","{\"openalex\":\"W2016331656\",\"doi\":\"10.1145/1201775.882267\"}","2026-05-16T03:34:56.443556466","2026-05-21T21:23:04.188515321","10.1145/1201775.882267","W2016331656",506,[166,167,168],"paper_W1984713107","{\"openalex\":\"W1984713107\",\"doi\":\"10.1145/1201775.882368\"}","2026-05-15T10:30:02.206495228","2026-05-21T07:30:06.187063771","10.1145/1201775.882368","W1984713107",623,[179,180,181],"paper_W2046658845","Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit","{\"openalex\":\"W2046658845\",\"doi\":\"10.1109/tit.2011.2173241\"}","2026-05-14T06:59:04.316695249","2026-05-21T09:42:54.191473967","10.1109/tit.2011.2173241","W2046658845","Finding the sparsest solution to underdetermined systems of linear equations y = Φ _x is NP-hard in general. We show here that for systems with “typical”/“random” Φ, a good approximation to the sparsest solution is obtained by applying a fixed number of standard operations from linear algebra. Our proposal, Stagewise Orthogonal Matching Pursuit (StOMP), successively transforms the signal into a negligible residual. Starting with initial residual r ₀ = y , at the s -th stage it forms the “matched filter” Φ T _rs-1 , identifies all coordinates with amplitudes exceeding a specially chosen threshold, solves a least-squares problem using the selected coordinates, and subtracts the least-squares fit, producing a new residual. After a fixed number of stages (e.g., 10), it stops. In contrast to Orthogonal Matching Pursuit (OMP), many coefficients can enter the model at each stage in StOMP while only one enters per stage in OMP; and StOMP takes a fixed number of stages (e.g., 10), while OMP can take many (e.g., n ). We give both theoretical and empirical support for the large-system effectiveness of StOMP. 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Iddo Drori 人物

Iddo Drori · 相关事件